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tensoreflusso:: ops:: LRN
#include <nn_ops.h>
Normalizzazione della risposta locale.
Riepilogo
Il tensore input
4-D viene trattato come una matrice 3-D di vettori 1-D (lungo l'ultima dimensione) e ciascun vettore viene normalizzato in modo indipendente. All'interno di un dato vettore, ogni componente viene diviso per la somma ponderata e quadrata degli input all'interno di depth_radius
. Nel dettaglio,
sqr_sum[a, b, c, d] =
sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta
Per i dettagli, vedere Krizhevsky et al., Classificazione ImageNet con reti neurali convoluzionali profonde (NIPS 2012) .
Argomenti:
- scope: un oggetto Scope
- ingresso: 4-D.
Attributi facoltativi (vedi Attrs
):
- raggio_profondità: 0-D. Metà larghezza della finestra di normalizzazione 1-D.
- bias: un offset (solitamente positivo per evitare la divisione per 0).
- alfa: un fattore di scala, solitamente positivo.
- beta: un esponente.
Resi:
-
Output
: il tensore di uscita.
Attributi pubblici
Funzioni pubbliche
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operatore::tensorflow::Output
operator::tensorflow::Output() const
Funzioni pubbliche statiche
Alfa
Attrs Alpha(
float x
)
Beta
Attrs Beta(
float x
)
Pregiudizio
Attrs Bias(
float x
)
ProfonditàRaggio
Attrs DepthRadius(
int64 x
)
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-27 UTC.
[null,null,["Ultimo aggiornamento 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::LRN Class Reference\n\ntensorflow::ops::LRN\n====================\n\n`#include \u003cnn_ops.h\u003e`\n\nLocal Response Normalization.\n\nSummary\n-------\n\nThe 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, \n\n```scdoc\nsqr_sum[a, b, c, d] =\n sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)\noutput = input / (bias + alpha * sqr_sum) ** beta\n```\n\n\u003cbr /\u003e\n\nFor details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).\n\nArguments:\n\n- scope: A [Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: 4-D.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs)):\n\n- depth_radius: 0-D. Half-width of the 1-D normalization window.\n- bias: An offset (usually positive to avoid dividing by 0).\n- alpha: A scale factor, usually positive.\n- beta: An exponent.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The output tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [LRN](#classtensorflow_1_1ops_1_1_l_r_n_1adbadf9462bc6ae9916f535bb2aa2762f)`(const ::`[tensorflow::Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input)` ||\n| [LRN](#classtensorflow_1_1ops_1_1_l_r_n_1ab702d3657c46710fcf7a63f7c712c5df)`(const ::`[tensorflow::Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, const `[LRN::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_l_r_n_1a001e6e41e5fb3ff78b42decdd680ea82) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_l_r_n_1a69396918e55e1de00f68a1113ef173b0) | `::`[tensorflow::Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|-----------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_l_r_n_1aa28d07232c5df13dad811653f1276a2a)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_l_r_n_1aa00d48e5a8ca805aa2532b7155b8c28b)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_l_r_n_1ae58da447d50c92abb12785d8ab7b618b)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|\n| [Alpha](#classtensorflow_1_1ops_1_1_l_r_n_1a7788a93182ddfbf8bb5bd1820b081392)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [Beta](#classtensorflow_1_1ops_1_1_l_r_n_1a6bbb26306e2265f6e2368f5dfb39ef13)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [Bias](#classtensorflow_1_1ops_1_1_l_r_n_1ac8da24639c0d90ef6e68df756f3e345f)`(float x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n| [DepthRadius](#classtensorflow_1_1ops_1_1_l_r_n_1ac579054901f30ab7fd4989ca39237a0e)`(int64 x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs#structtensorflow_1_1ops_1_1_l_r_n_1_1_attrs) |\n\n| ### Structs ||\n|---------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::LRN::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/l-r-n/attrs) | Optional attribute setters for [LRN](/versions/r2.2/api_docs/cc/class/tensorflow/ops/l-r-n#classtensorflow_1_1ops_1_1_l_r_n). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### LRN\n\n```gdscript\n LRN(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input\n)\n``` \n\n### LRN\n\n```gdscript\n LRN(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n const LRN::Attrs & attrs\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n``` \n\nPublic static functions\n-----------------------\n\n### Alpha\n\n```text\nAttrs Alpha(\n float x\n)\n``` \n\n### Beta\n\n```text\nAttrs Beta(\n float x\n)\n``` \n\n### Bias\n\n```text\nAttrs Bias(\n float x\n)\n``` \n\n### DepthRadius\n\n```text\nAttrs DepthRadius(\n int64 x\n)\n```"]]